Detecting Potential Violent Behavior Using Deep Learning

Dalton Chukwuezugo Owoh

Master's thesis

Abstract:

In this master's thesis, four deep learning models - DenseNet-121, Inception-v3, ResNet50, and VGG-16 were implemented to detect potential violent behavior by applying transfer learning principles. In the theoretical part, a comprehensive review of literature in the field of human violence detection was conducted to identify prevalent strengths and gaps in existing research work. The results of the experiments conducted in this work showed the best performance with accuracy values of 98%. This work recommends, among other key findings, that future research be geared towards exploring the generalization of results from this experiment across larger datasets with adaptations to a broader domain. The future work will also benefit greatly from further hyperparameter tuning of models with more configurations.

Abstract:

In this master's thesis, four deep learning models - DenseNet-121, Inception-v3, ResNet50, and VGG-16 were implemented to detect potential violent behavior by applying transfer learning principles. In the theoretical part, a comprehensive review of literature in the field of human violence detection was conducted to identify prevalent strengths and gaps in existing research work. The results of the experiments conducted in this work showed the best performance with accuracy values of 98%. This work recommends, among other key findings, that future research be geared towards exploring the generalization of results from this experiment across larger datasets with adaptations to a broader domain. The future work will also benefit greatly from further hyperparameter tuning of models with more configurations.

Keywords

Violence detection, AI, Hyperparameter tuning, Pattern recognition, Convolutional Neural Networks

Language used: English

Date on which the thesis was submitted / produced: 13. 5. 2024

Thesis defence

• Supervisor: prof. Ing. Roman Šenkeřík, Ph.D.

Tomas Bata University in Zlín

Faculty of Applied Informatics

Master programme / field:
Information Technologies / Software Engineering